In the era where social media significantly influences public sentiment, platforms such as Twitter have become vital in predicting stock market trends. This paper presents a cutting-edge predictive model that integrat...
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Delay/disruption tolerant networking(DTN) is proposed as a networking architecture to overcome challenging space communication characteristics for reliable data transmission service in presence of long propagation del...
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Delay/disruption tolerant networking(DTN) is proposed as a networking architecture to overcome challenging space communication characteristics for reliable data transmission service in presence of long propagation delays and/or lengthy link disruptions. Bundle protocol(BP) and Licklider Transmission Protocol(LTP) are the main key technologies for DTN. LTP red transmission offers a reliable transmission mechanism for space networks. One of the key metrics used to measure the performance of LTP in space applications is the end-to-end data delivery delay, which is influenced by factors such as the quality of spatial channels and the size of cross-layer packets. In this paper, an end-to-end reliable data delivery delay model of LTP red transmission is proposed using a roulette wheel algorithm, and the roulette wheel algorithm is more in line with the typical random characteristics in space networks. The proposed models are validated through real data transmission experiments on a semi-physical testing platform. Furthermore, the impact of cross-layer packet size on the performance of LTP reliable transmission is analyzed, with a focus on bundle size, block size, and segment size. The analysis and study results presented in this paper offer valuable contributions towards enhancing the reliability of LTP transmission in space communication scenarios.
Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands subst...
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Object detection and image restoration pose significant challenges in deep learning and computer vision. These tasks are widely employed in various applications, and there is an increasing demand for specialized envir...
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During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of p...
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During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patientscan ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT).The IoMT can monitor and transmit sensitive health data;however, it may be vulnerable to various attacks. In thispaper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, themedical sensors can transmit sensed encrypted health data via a mobile application to the doctor for ***, three consortium blockchains are constructed for load balancing of transactions and reducing transactionlatency. They store the credentials of system entities, doctors' prescriptions and recommendations according to thedata transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics areused for providing authentication and increasing the security of the proposed medical system. The investigationalresults show that the proposed system outperforms existing work where the proposed model consumed lessprocessing time by values of 18%, 22%, and 40%, and less energy for processing a 200 KB file by values of 9%,13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. Fromthese results, it is clear that the proposed system gives a very reliable and secure performance for efficientlysecuring medical applications.
To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this *** addressed problem correlates to the third Sustainabl...
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To detect the improper sitting posture of a person sitting on a chair,a posture detection system using machine learning classification has been proposed in this *** addressed problem correlates to the third Sustainable Development Goal(SDG),ensuring healthy lives and promoting well-being for all ages,as specified by the World Health Organization(WHO).An improper sitting position can be fatal if one sits for a long time in the wrong position,and it can be dangerous for ulcers and lower spine *** novel study includes a practical implementation of a cushion consisting of a grid of 3×3 force-sensitive resistors(FSR)embedded to read the pressure of the person sitting on ***,the Body Mass Index(BMI)has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures(backward,front,left,and right-leaning)based on the five machine learning algorithms:ensemble boosted trees,ensemble bagged trees,ensemble subspace K-Nearest Neighbors(KNN),ensemble subspace discriminant,and ensemble RUSBoosted *** proposed arrangement is novel as existing works have only provided simulations without practical implementation,whereas we have implemented the proposed design in *** results validate the proposed sensor placements,and the machine learning(ML)model reaches a maximum accuracy of 99.99%,which considerably outperforms the existing *** proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.
Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spec...
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This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initia...
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Age-related Macular Degeneration (AMD) is a leading cause of visual impairment among the elderly worldwide. This study compares deep learning-based and classical feature extraction methods for AMD classification using...
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In the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is sig...
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